INMT: Interactive Neural Machine Translation Prediction
Sebastin Santy, Sandipan Dandapat, Monojit Choudhury, Kalika Bali
Abstract
In this paper, we demonstrate an Interactive Machine Translation interface, that assists human translators with on-the-fly hints and suggestions. This makes the end-to-end translation process faster, more efficient and creates high-quality translations. We augment the OpenNMT backend with a mechanism to accept the user input and generate conditioned translations.- Anthology ID:
- D19-3018
- Volume:
- Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Editors:
- Sebastian Padó, Ruihong Huang
- Venues:
- EMNLP | IJCNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 103–108
- Language:
- URL:
- https://aclanthology.org/D19-3018
- DOI:
- 10.18653/v1/D19-3018
- Cite (ACL):
- Sebastin Santy, Sandipan Dandapat, Monojit Choudhury, and Kalika Bali. 2019. INMT: Interactive Neural Machine Translation Prediction. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations, pages 103–108, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- INMT: Interactive Neural Machine Translation Prediction (Santy et al., EMNLP-IJCNLP 2019)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/D19-3018.pdf
- Code
- microsoft/inmt